Artificial Intelligence Data Sets Based Machine Learning Approach in Health Sciences

Abstract

Detection and diagnosis related to medical health sciences has always been a point of investigations for prolific researchers. The crave for saving human lives and relevant developmental revolutions based on technological innovations and support has taken a giant leap in this decade. The present decade is witnessing a widely accepted integration of engineering domain based artificial intelligence tools in providing instant remedies and assistance. Professionals are seen exerting in health and hygiene domain for saving  the most precious creation on the earth i.e. human lives. The present study is a small effort in the same direction and compares the novel artificial intelligence based technique and traditional techniques in detection and diagnosis of human body ailments in real or referral time. Machine learning approach has been verified which fundamentally uses artificial intelligence in which a large amount of data set is processed using a well established algorithm.  Usually, such algorithm uses data sets in which the first set is known as trained data set which is used to train the machine with logical derived algorithm. The another data set is the observed data set. These two data sets are compared either in real time or referral time and accordingly the gap sets forth the presence or absence of any disease. The present study observed a very high level of accuracy using machine learning. Secondly, apart from accuracy it ensures the speediest possible assistance. Machine Learning Technique popularly known as MLT, helps predicting the outcome relevant to presence or absence of any disease. In the study Decision Tree and SVM have been analysed which are being used which are well supported by python machine learning modules like pandas, sklearn, Seaborn.